In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

[Q]: We collect $37,263$ concept-sets as the inputs, each of which contains three to five common concepts. These concept-sets are sampled from several large corpora of image/video captions, such that the concepts inside them are more likely to co-occur in natural scenes. The expected concept-sets in our task are supposed to be likely co-occur in natural, daily-life scenes . The concepts in images/videos captions, which usually describe scenes in our daily life, thus possess the desired property. We therefore collect a large amount of caption sentences from a variety of datasets, including VATEX BIBREF4, LSMDC BIBREF12, ActivityNet BIBREF13, and SNLI BIBREF15, forming 1,040,330 sentences in total. 
 Question: Where do the concept sets come from?
[A]: These concept-sets are sampled from several large corpora of image/video captions


[Q]: These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text. 
 Question: How do they extract topics?
[A]:  automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15


[Q]: Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'. 
 Question: How do they extract causality from text?
[A]:
They identify documents that contain the unigrams 'caused', 'causing', or 'causes'


